Image Classification
Keras
LiteRT
TF-Keras
Safetensors
English
efficientnetv2-s
efficientnetv2
fgic
transfer-learning
gem-pooling
focal-loss
swa
grad-cam
calibration
temperature-scaling
computer-vision
tensorflow.js
Eval Results (legacy)
Instructions to use 0xgr3y/Arch-Building-Image-Classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Keras
How to use 0xgr3y/Arch-Building-Image-Classification with Keras:
# Available backend options are: "jax", "torch", "tensorflow". import os os.environ["KERAS_BACKEND"] = "jax" import keras model = keras.saving.load_model("hf://0xgr3y/Arch-Building-Image-Classification") - Notebooks
- Google Colab
- Kaggle
| { | |
| "image_processing": { | |
| "size": { | |
| "width": 320, | |
| "height": 320 | |
| }, | |
| "resample": "bilinear", | |
| "normalize": true, | |
| "mode": "efficientnet_v2_preprocess_input", | |
| "channel_order": "RGB", | |
| "mean": [ | |
| 0.0, | |
| 0.0, | |
| 0.0 | |
| ], | |
| "std": [ | |
| 1.0, | |
| 1.0, | |
| 1.0 | |
| ], | |
| "scale": 1.0, | |
| "description": "preprocess_input is identity in TF 2.12+; EfficientNetV2-S includes internal Rescaling layer. Input expects raw [0, 255] float32." | |
| }, | |
| "input_name": "input_1", | |
| "output_name": "dense_1", | |
| "input_shape": [ | |
| 1, | |
| 320, | |
| 320, | |
| 3 | |
| ], | |
| "output_shape": [ | |
| 1, | |
| 8 | |
| ] | |
| } |